



'''simple docstring'''

import unittest

from parameterized import parameterized

from transformers import AutoTokenizer, GPTNeoXConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, slow, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
			import torch

			from transformers import (
			    GPTNeoXForCausalLM,
			    GPTNeoXForQuestionAnswering,
			    GPTNeoXForSequenceClassification,
			    GPTNeoXForTokenClassification,
			    GPTNeoXModel,
			)

class 							a__:






			def __init__(     self					,   _UpperCAmelCase					,   _UpperCAmelCase=13					,   _UpperCAmelCase=7					,   _UpperCAmelCase=True					,   _UpperCAmelCase=True					,   _UpperCAmelCase=True					,   _UpperCAmelCase=True					,   _UpperCAmelCase=99					,   _UpperCAmelCase=64					,   _UpperCAmelCase=5					,   _UpperCAmelCase=4					,   _UpperCAmelCase=37					,   _UpperCAmelCase="gelu"					,   _UpperCAmelCase=0.1					,   _UpperCAmelCase=0.1					,   _UpperCAmelCase=512					,   _UpperCAmelCase=16					,   _UpperCAmelCase=2					,   _UpperCAmelCase=0.02					,   _UpperCAmelCase=3					,   _UpperCAmelCase=4					,   _UpperCAmelCase=None					,   )							->							Tuple:
					snake_case__	     =parent
					snake_case__	     =batch_size
					snake_case__	     =seq_length
					snake_case__	     =is_training
					snake_case__	     =use_input_mask
					snake_case__	     =use_token_type_ids
					snake_case__	     =use_labels
					snake_case__	     =vocab_size
					snake_case__	     =hidden_size
					snake_case__	     =num_hidden_layers
					snake_case__	     =num_attention_heads
					snake_case__	     =intermediate_size
					snake_case__	     =hidden_act
					snake_case__	     =hidden_dropout_prob
					snake_case__	     =attention_probs_dropout_prob
					snake_case__	     =max_position_embeddings
					snake_case__	     =type_vocab_size
					snake_case__	     =type_sequence_label_size
					snake_case__	     =initializer_range
					snake_case__	     =num_labels
					snake_case__	     =num_choices
					snake_case__	     =scope
					snake_case__	     =vocab_size - 1






			def 						_lowercase  (     self						)							->							str:
					snake_case__	     =ids_tensor([self.batch_size, self.seq_length]					,   self.vocab_size						)

					snake_case__	     =None
					if self.use_input_mask:
							snake_case__	     =random_attention_mask([self.batch_size, self.seq_length]						)

					snake_case__	     =None
					if self.use_labels:
							snake_case__	     =ids_tensor([self.batch_size, self.seq_length]					,   self.num_labels						)

					snake_case__	     =self.get_config()

					return config, input_ids, input_mask, token_labels






			def 						_lowercase  (     self						)							->							int:
					return GPTNeoXConfig(
					    vocab_size=self.vocab_size					,   hidden_size=self.hidden_size					,   num_hidden_layers=self.num_hidden_layers					,   num_attention_heads=self.num_attention_heads					,   intermediate_size=self.intermediate_size					,   hidden_act=self.hidden_act					,   hidden_dropout_prob=self.hidden_dropout_prob					,   attention_probs_dropout_prob=self.attention_probs_dropout_prob					,   max_position_embeddings=self.max_position_embeddings					,   type_vocab_size=self.type_vocab_size					,   is_decoder=_UpperCAmelCase					,   initializer_range=self.initializer_range					,   pad_token_id=self.pad_token_id					,   )






			def 						_lowercase  (     self						)							->							Any:
					snake_case__     ,	snake_case__     ,	snake_case__     ,	snake_case__	     =self.prepare_config_and_inputs()

					snake_case__	     =True

					return config, input_ids, input_mask, token_labels






			def 						_lowercase  (     self					,   _UpperCAmelCase					,   _UpperCAmelCase					,   _UpperCAmelCase						)							->							Any:
					snake_case__	     =GPTNeoXModel(config=_UpperCAmelCase						)
					model.to(_UpperCAmelCase						)
					model.eval()
					snake_case__	     =model(_UpperCAmelCase					,   attention_mask=_UpperCAmelCase						)
					snake_case__	     =model(_UpperCAmelCase						)
					self.parent.assertEqual(result.last_hidden_state.shape					,   (self.batch_size, self.seq_length, self.hidden_size)						)






			def 						_lowercase  (     self					,   _UpperCAmelCase					,   _UpperCAmelCase					,   _UpperCAmelCase						)							->							Any:
					snake_case__	     =True
					snake_case__	     =GPTNeoXModel(_UpperCAmelCase						)
					model.to(_UpperCAmelCase						)
					model.eval()
					snake_case__	     =model(_UpperCAmelCase					,   attention_mask=_UpperCAmelCase						)
					self.parent.assertEqual(result.last_hidden_state.shape					,   (self.batch_size, self.seq_length, self.hidden_size)						)






			def 						_lowercase  (     self					,   _UpperCAmelCase					,   _UpperCAmelCase					,   _UpperCAmelCase					,   _UpperCAmelCase						)							->							Optional[int]:
					snake_case__	     =GPTNeoXForCausalLM(config=_UpperCAmelCase						)
					model.to(_UpperCAmelCase						)
					model.eval()
					snake_case__	     =model(_UpperCAmelCase					,   attention_mask=_UpperCAmelCase					,   labels=_UpperCAmelCase						)
					self.parent.assertEqual(result.logits.shape					,   (self.batch_size, self.seq_length, self.vocab_size)						)






			def 						_lowercase  (     self					,   _UpperCAmelCase					,   _UpperCAmelCase					,   _UpperCAmelCase					,   _UpperCAmelCase						)							->							str:
					snake_case__	     =self.num_labels
					snake_case__	     =GPTNeoXForQuestionAnswering(_UpperCAmelCase						)
					model.to(_UpperCAmelCase						)
					model.eval()
					snake_case__	     =model(_UpperCAmelCase					,   attention_mask=_UpperCAmelCase						)
					self.parent.assertEqual(result.start_logits.shape					,   (self.batch_size, self.seq_length)						)
					self.parent.assertEqual(result.end_logits.shape					,   (self.batch_size, self.seq_length)						)






			def 						_lowercase  (     self					,   _UpperCAmelCase					,   _UpperCAmelCase					,   _UpperCAmelCase					,   _UpperCAmelCase						)							->							Dict:
					snake_case__	     =self.num_labels
					snake_case__	     =GPTNeoXForSequenceClassification(_UpperCAmelCase						)
					model.to(_UpperCAmelCase						)
					model.eval()
					snake_case__	     =ids_tensor([self.batch_size]					,   self.type_sequence_label_size						)
					snake_case__	     =model(_UpperCAmelCase					,   attention_mask=_UpperCAmelCase					,   labels=_UpperCAmelCase						)
					self.parent.assertEqual(result.logits.shape					,   (self.batch_size, self.num_labels)						)






			def 						_lowercase  (     self					,   _UpperCAmelCase					,   _UpperCAmelCase					,   _UpperCAmelCase					,   _UpperCAmelCase						)							->							List[str]:
					snake_case__	     =self.num_labels
					snake_case__	     =GPTNeoXForTokenClassification(_UpperCAmelCase						)
					model.to(_UpperCAmelCase						)
					model.eval()
					snake_case__	     =model(_UpperCAmelCase					,   attention_mask=_UpperCAmelCase					,   labels=_UpperCAmelCase						)
					self.parent.assertEqual(result.logits.shape					,   (self.batch_size, self.seq_length, self.num_labels)						)






			def 						_lowercase  (     self					,   _UpperCAmelCase					,   _UpperCAmelCase					,   _UpperCAmelCase						)							->							Optional[Any]:
					snake_case__	     =True
					snake_case__	     =GPTNeoXForCausalLM(config=_UpperCAmelCase						)
					model.to(_UpperCAmelCase						)
					model.eval()

					# first forward pass
					snake_case__	     =model(_UpperCAmelCase					,   attention_mask=_UpperCAmelCase					,   use_cache=_UpperCAmelCase						)
					snake_case__	     =outputs.past_key_values

					# create hypothetical multiple next token and extent to next_input_ids
					snake_case__	     =ids_tensor((self.batch_size, 3)					,   config.vocab_size						)
					snake_case__	     =ids_tensor((self.batch_size, 3)					,   vocab_size=2						)

					# append to next input_ids and
					snake_case__	     =torch.cat([input_ids, next_tokens]					,   dim=-1						)
					snake_case__	     =torch.cat([input_mask, next_mask]					,   dim=-1						)

					snake_case__	     =model(_UpperCAmelCase					,   attention_mask=_UpperCAmelCase					,   output_hidden_states=_UpperCAmelCase						)
					snake_case__	     =output_from_no_past['hidden_states'][0]
					snake_case__	     =model(
					    _UpperCAmelCase					,   attention_mask=_UpperCAmelCase					,   past_key_values=_UpperCAmelCase					,   output_hidden_states=_UpperCAmelCase					,   )['hidden_states'][0]

					# select random slice
					snake_case__	     =ids_tensor((1,)					,   output_from_past.shape[-1]						).item()
					snake_case__	     =output_from_no_past[:, -3:, random_slice_idx].detach()
					snake_case__	     =output_from_past[:, :, random_slice_idx].detach()

					self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1]						)

					# test that outputs are equal for slice
					self.parent.assertTrue(torch.allclose(_UpperCAmelCase					,   _UpperCAmelCase					,   atol=1E-3						)						)






			def 						_lowercase  (     self						)							->							Union[str, Any]:
					snake_case__	     =self.prepare_config_and_inputs()
					snake_case__     ,	snake_case__     ,	snake_case__     ,	snake_case__	     =config_and_inputs
					snake_case__	     ={'input_ids': input_ids, 'attention_mask': input_mask}
					return config, inputs_dict





@require_torch
class 							a__( snake_case__   ,      snake_case__   ,      snake_case__   ,      unittest.TestCase     ):
			a_	: List[str]									=      (
			    (
			        GPTNeoXModel,
			        GPTNeoXForCausalLM,
			        GPTNeoXForQuestionAnswering,
			        GPTNeoXForSequenceClassification,
			        GPTNeoXForTokenClassification,
			    )
			    if is_torch_available()
			    else ()
			)
			a_	: str									=      (GPTNeoXForCausalLM,) if is_torch_available() else ()
			a_	: Optional[Any]									=      (
			    {
			        '''feature-extraction''': GPTNeoXModel,
			        '''question-answering''': GPTNeoXForQuestionAnswering,
			        '''text-classification''': GPTNeoXForSequenceClassification,
			        '''text-generation''': GPTNeoXForCausalLM,
			        '''token-classification''': GPTNeoXForTokenClassification,
			        '''zero-shot''': GPTNeoXForSequenceClassification,
			    }
			    if is_torch_available()
			    else {}
			)
			a_	: List[str]									=      False
			a_	: Any									=      False
			a_	: List[str]									=      False
			a_	: Dict									=      False






			def 						_lowercase  (     self						)							->							str:
					snake_case__	     =GPTNeoXModelTester(self						)
					snake_case__	     =ConfigTester(self					,   config_class=_UpperCAmelCase					,   hidden_size=64					,   num_attention_heads=8						)






			def 						_lowercase  (     self						)							->							int:
					self.config_tester.run_common_tests()






			def 						_lowercase  (     self						)							->							List[Any]:
					snake_case__     ,	snake_case__     ,	snake_case__     ,	snake_case__	     =self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_model(_UpperCAmelCase					,   _UpperCAmelCase					,   _UpperCAmelCase						)






			def 						_lowercase  (     self						)							->							List[str]:
					snake_case__     ,	snake_case__     ,	snake_case__     ,	snake_case__	     =self.model_tester.prepare_config_and_inputs_for_decoder()
					self.model_tester.create_and_check_model_as_decoder(_UpperCAmelCase					,   _UpperCAmelCase					,   _UpperCAmelCase						)






			def 						_lowercase  (     self						)							->							Optional[Any]:
					# This regression test was failing with PyTorch < 1.3
					snake_case__     ,	snake_case__     ,	snake_case__     ,	snake_case__	     =self.model_tester.prepare_config_and_inputs_for_decoder()

					snake_case__	     =None

					self.model_tester.create_and_check_model_as_decoder(_UpperCAmelCase					,   _UpperCAmelCase					,   _UpperCAmelCase						)






			def 						_lowercase  (     self						)							->							List[Any]:
					snake_case__     ,	snake_case__     ,	snake_case__     ,	snake_case__	     =self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_decoder_model_past_large_inputs(_UpperCAmelCase					,   _UpperCAmelCase					,   _UpperCAmelCase						)






			def 						_lowercase  (     self						)							->							Union[str, Any]:
					snake_case__	     =self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_for_causal_lm(*_UpperCAmelCase						)






			def 						_lowercase  (     self						)							->							Tuple:
					snake_case__	     =self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_for_question_answering(*_UpperCAmelCase						)






			def 						_lowercase  (     self						)							->							str:
					snake_case__	     =self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_for_sequence_classification(*_UpperCAmelCase						)






			def 						_lowercase  (     self						)							->							Tuple:
					snake_case__	     =self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_for_token_classification(*_UpperCAmelCase						)






			@unittest.skip(reason='Feed forward chunking is not implemented'						)
			def 						_lowercase  (     self						)							->							Tuple:
					pass






			@parameterized.expand([('linear',), ('dynamic',)]						)
			def 						_lowercase  (     self					,   _UpperCAmelCase						)							->							List[Any]:
					snake_case__     ,	snake_case__	     =self.model_tester.prepare_config_and_inputs_for_common()
					snake_case__	     =ids_tensor([1, 10]					,   config.vocab_size						)
					snake_case__	     =ids_tensor([1, int(config.max_position_embeddings * 1.5						)]					,   config.vocab_size						)

					set_seed(42						)  # Fixed seed at init time so the two models get the same random weights
					snake_case__	     =GPTNeoXModel(_UpperCAmelCase						)
					original_model.to(_UpperCAmelCase						)
					original_model.eval()
					snake_case__	     =original_model(_UpperCAmelCase						).last_hidden_state
					snake_case__	     =original_model(_UpperCAmelCase						).last_hidden_state

					set_seed(42						)  # Fixed seed at init time so the two models get the same random weights
					snake_case__	     ={'type': scaling_type, 'factor': 10.0}
					snake_case__	     =GPTNeoXModel(_UpperCAmelCase						)
					scaled_model.to(_UpperCAmelCase						)
					scaled_model.eval()
					snake_case__	     =scaled_model(_UpperCAmelCase						).last_hidden_state
					snake_case__	     =scaled_model(_UpperCAmelCase						).last_hidden_state

					# Dynamic scaling does not change the RoPE embeddings until it receives an input longer than the original
					# maximum sequence length, so the outputs for the short input should match.
					if scaling_type == "dynamic":
							self.assertTrue(torch.allclose(_UpperCAmelCase					,   _UpperCAmelCase					,   atol=1E-5						)						)
					else:
							self.assertFalse(torch.allclose(_UpperCAmelCase					,   _UpperCAmelCase					,   atol=1E-5						)						)

					# The output should be different for long inputs
					self.assertFalse(torch.allclose(_UpperCAmelCase					,   _UpperCAmelCase					,   atol=1E-5						)						)





@require_torch
class 							a__( unittest.TestCase     ):






			@slow
			def 						_lowercase  (     self						)							->							Dict:
					snake_case__	     =AutoTokenizer.from_pretrained('EleutherAI/pythia-410m-deduped'						)
					for checkpointing in [True, False]:
							snake_case__	     =GPTNeoXForCausalLM.from_pretrained('EleutherAI/pythia-410m-deduped'						)

							if checkpointing:
									model.gradient_checkpointing_enable()
							else:
									model.gradient_checkpointing_disable()
							model.to(_UpperCAmelCase						)

							snake_case__	     =tokenizer('My favorite food is'					,   return_tensors='pt'						).to(_UpperCAmelCase						)
							# The hub repo. is updated on 2023-04-04, resulting in poor outputs.
							# See: https://github.com/huggingface/transformers/pull/24193
							snake_case__	     ='My favorite food is a good old-fashioned, old-fashioned, old-fashioned.\n\nI\'m not sure'

							snake_case__	     =model.generate(**_UpperCAmelCase					,   do_sample=_UpperCAmelCase					,   max_new_tokens=20						)
							snake_case__	     =tokenizer.batch_decode(_UpperCAmelCase						)[0]

							self.assertEqual(_UpperCAmelCase					,   _UpperCAmelCase						)









'''simple docstring'''

import warnings

from ...utils import logging
from .image_processing_glpn import GLPNImageProcessor


SCREAMING_SNAKE_CASE__     :						Optional[Any]	=  logging.get_logger(__name__)

class 							a__( snake_case__     ):






			def __init__(     self					,   *_UpperCAmelCase					,   **_UpperCAmelCase						)							->							None:
					warnings.warn(
					    'The class GLPNFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'
					    ' use GLPNImageProcessor instead.'					,   _UpperCAmelCase					,   )
					super().__init__(*_UpperCAmelCase					,   **_UpperCAmelCase						)




